Snowflake

Global – October 27, 2026 -- Alteryx is marketing its platform as the governed business-logic layer on top of Snowflake's AI Data Cloud. It cites a 90% time saving in campaign development at Telenet and a 25% subscriber increase at BODi as proof points.

Alteryx pushes transformation logic into Snowflake while business users own the rules

Alteryx pushes transformation logic down into Snowflake for performance. It gives business users a no-code way to build and govern the rules that turn raw data into decisions. The company's argument is that Snowflake stores and scales data, but a warehouse does not know definitions such as a "qualified pipeline" or an "at-risk account." Without those rules made explicit, an LLM or AI agent querying the data is left to guess, producing inconsistent answers and hallucinations.

Five use cases span finance, procurement, customer success and inventory

Alteryx names five patterns:

Global – October 27, 2026 -- Snowflake's sales finance team has cut its monthly forecast and variance analysis from more than a week to a few hours. It did so by giving its CoCo AI standing context about company data, terminology and workflows.

Profile cuts steps per answer from 3.0 to 1.3

Across 44 finance users, those with a 'GTM Finance' profile reached an answer in about 1.3 steps, against about 3 steps for those without one (Snowflake internal CoCo usage, February to May 2026). Without the profile, the AI spent roughly three steps per session finding the right data source and resolving terminology.

Each extra step adds about 0.05 credits, roughly 10 cents at US$2 per credit. A five-step session therefore costs about three and a half times a one-step session. At every step count in the data, sessions with the profile cost less than those without.

North America – October 27, 2026 -- Snowflake's product data science team cut the time from pipeline incident to a tested, reviewable fix to under 16 minutes using its CoCo agentic AI assistant. Before CoCo, diagnosis took a skilled engineer about 30 minutes per case. The result comes from a four-part framework that Snowflake built to measure AI value beyond license counts and prompt volumes.

Snowflake sorts AI work into three levels instead of counting prompts

Dashboards built on access and activity score an organization running 1,000 explanations the same as one running 1,000 pipeline builds and anomaly investigations. Snowflake's framework separates foundational work (explaining code, answering questions), applied development (writing code, exploring data, building reports) and agentic work (multistep processes where AI uses tools to finish an outcome). Real capacity gains should appear in the agentic tier.

United States – – October 27, 2026 -- Brilliant Earth has moved more than 100 showroom operations users off a legacy BI platform and onto Posit dashboards running natively inside Snowflake, cutting software license costs while widening self-serve access to governed data.

The luxury jewelry retailer already reprices its diamond inventory daily through an automated model running in Snowflake. That capability prompted the company to adopt the platform. Weekly data science reports captured only moments in time, so Brilliant Earth set out to give decision-makers direct access to the same data and models.

Posit Team runs as a Snowflake Native App on Snowpark Container Services

Brilliant Earth installed Posit Team inside Snowflake instead of maintaining a separate server with its own security protocols. Snowflake roles, user permissions and table access now govern Posit as well.

Brazil – October 27, 2026 -- Brazilian flat glass maker Vivix expects to cut furnace energy consumption by 3%-5%, extend furnace lifespan by one year and shorten its data collection and analytics cycle by 80%, based on a pilot of an executable digital twin (xDT) built with Siemens, Snowflake and NVIDIA.

Vivix targets blind spots in 1,550°C-1,600°C float-glass furnaces

The furnace is one of the most critical and hardest-to-observe assets in float-glass production. At operating temperatures of roughly 1,550°C to 1,600°C, teams have limited direct visibility into how combustion gases, molten glass and refractory walls interact. Vivix supplies glass and mirrors to the construction and furniture markets.

Global – October 27, 2026 -- Dust, a collaborative AI platform, is targeting three forms of enterprise AI dependency: reliance on a single model provider, loss of control over agent-generated data, and limited visibility into costs and behavior, according to Co-Founder and CTO Stanislas Polu.

Dust builds a shared layer between staff, company data and multiple models

Polu said Dust lets teams work with AI, switch among supported frontier and open source models, and govern the context, agents and traces their work generates. The platform is multimodel by design, so companies can change models without rebuilding their context and workflows.

Polu argues that agent traces, which record instructions, context, model and tool choices, outputs and human feedback, may become one of a company's most valuable AI assets. He said that corpus could later help companies evaluate or train models for their specific work.

Global – October 27, 2026 -- Snowflake is built around three pillars, Ground, Analyze and Act, wrapped in a governance layer it calls Assure, to let AI agents run open-ended business investigations on enterprise data without copying that data into each agent platform.

The model targets agents that decide which data to examine, learn from each result and choose the next step. A churn probe, for example, could compare customer segments, product usage and support issues, then feed a recommended response into a retention workflow.

Portland – October 27, 2026 -- Multnomah County, Oregon, migrated a decade-old SQL Server environment to a modern data platform in five months, a timeline that public sector organizations often stretch over years, according to a session at this year's Snowflake Summit.

Multnomah County replaces undocumented SQL Server estate with AI-assisted migration

The county's legacy environment spanned dozens of business intelligence workloads and had been built by engineers who had since left. Documentation was sparse, the architecture was inconsistent and costs were climbing.

Irvine – October 13, 2026 -- ATTOM has released version 1.0 of Semantic Views for its property data delivered through Snowflake, adding a layer of business context intended to help AI systems interpret and query complex real estate datasets.

ATTOM builds the semantic layer on Snowflake's native technology

The offering uses Snowflake's native Semantic View technology. It defines how ATTOM's property data points connect and should be used, so customers no longer have to build and maintain that knowledge themselves. ATTOM said it drew on decades of experience curating and standardizing property data.

Four customer benefits target AI development speed and accuracy

ATTOM lists four intended outcomes: